{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[this doc on github](https://github.com/dotnet/interactive/tree/main/samples/notebooks/fsharp/Samples)\n",
    "\n",
    "# Machine Learning over House Prices with ML.NET\n",
    "\n",
    "### Reference the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "fsharp"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Installed package Microsoft.ML version 1.4.0"
     },
     "output_type": "unknown"
    },
    {
     "data": {
      "text/plain": "Installed package Microsoft.Data.Analysis version 0.2.0"
     },
     "output_type": "unknown"
    },
    {
     "data": {
      "text/plain": "Installed package Microsoft.ML.AutoML version 0.16.0"
     },
     "output_type": "unknown"
    },
    {
     "data": {
      "text/plain": "Installed package XPlot.Plotly.Interactive version 4.0.2"
     },
     "output_type": "unknown"
    },
    {
     "data": {
      "text/markdown": "Loading extensions from `XPlot.Plotly.Interactive.dll`"
     },
     "output_type": "unknown"
    },
    {
     "data": {
      "text/markdown": "Configuring PowerShell Kernel for XPlot.Plotly integration."
     },
     "output_type": "unknown"
    },
    {
     "data": {
      "text/markdown": "Installed support for XPlot.Plotly."
     },
     "output_type": "unknown"
    }
   ],
   "source": [
    "#r \"nuget:Microsoft.ML,1.4.0\"\r\n",
    "#r \"nuget:Microsoft.ML.AutoML,0.16.0\"\r\n",
    "#r \"nuget:Microsoft.Data.Analysis,0.2.0\"\r\n",
    "#r \"nuget: XPlot.Plotly.Interactive, 4.0.6\"\r\n",
    "    \r\n",
    "open Microsoft.Data.Analysis\r\n",
    "open XPlot.Plotly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding better default formatting for data frames\n",
    "\n",
    "Register a formatter for data frames and data frame rows.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "fsharp"
    }
   },
   "outputs": [],
   "source": [
    "module DateFrameFormatter = \r\n",
    "    \r\n",
    "    // Locally open the F# HTML DSL.\r\n",
    "    open Html\r\n",
    "\r\n",
    "    let maxRows = 20\r\n",
    "\r\n",
    "    Formatter.Register<DataFrame>((fun (df: DataFrame) (writer: TextWriter) ->\r\n",
    "\r\n",
    "        let take = 20\r\n",
    "        table [] [\r\n",
    "          thead [] [\r\n",
    "            th [] [ str \"Index\" ]\r\n",
    "            for c in df.Columns do\r\n",
    "              th [] [ str c.Name]\r\n",
    "          ]\r\n",
    "          tbody [] [\r\n",
    "            for i in 0 .. min maxRows (int df.Rows.Count - 1) do\r\n",
    "              tr [] [\r\n",
    "                td [] [ i ]\r\n",
    "                for o in df.Rows.[int64 i] do\r\n",
    "                  td [] [ o ]\r\n",
    "              ]\r\n",
    "          ]\r\n",
    "        ]\r\n",
    "        |> writer.Write\r\n",
    "\r\n",
    "    ), mimeType = \"text/html\")\r\n",
    "    \r\n",
    "    Formatter.Register<DataFrameRow>((fun (row: DataFrameRow) (writer: TextWriter) ->\r\n",
    "\r\n",
    "        table [] [\r\n",
    "          tbody [] [\r\n",
    "            tr [] [\r\n",
    "              for o in row do\r\n",
    "                td [] [ o ] \r\n",
    "            ]\r\n",
    "          ]\r\n",
    "        ]\r\n",
    "        |> writer.Write\r\n",
    "\r\n",
    "    ), mimeType = \"text/html\")\r\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "fsharp"
    }
   },
   "outputs": [],
   "source": [
    "open System.Net.Http\n",
    "let housingPath = \"housing.csv\"\n",
    "if not(File.Exists(housingPath)) then\n",
    "    let contents = HttpClient().GetStringAsync(\"https://raw.githubusercontent.com/ageron/handson-ml2/master/datasets/housing/housing.csv\").Result\n",
    "    File.WriteAllText(\"housing.csv\", contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add the data to the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "fsharp"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": "<table><thead><th>Index</th><th>longitude</th><th>latitude</th><th>housing_median_age</th><th>total_rooms</th><th>total_bedrooms</th><th>population</th><th>households</th><th>median_income</th><th>median_house_value</th><th>ocean_proximity</th></thead><tbody><tr><td>0</td><td>-122.23</td><td>37.88</td><td>41</td><td>880</td><td>129</td><td>322</td><td>126</td><td>8.3252</td><td>452600</td><td>NEAR BAY</td></tr><tr><td>1</td><td>-122.22</td><td>37.86</td><td>21</td><td>7099</td><td>1106</td><td>2401</td><td>1138</td><td>8.3014</td><td>358500</td><td>NEAR BAY</td></tr><tr><td>2</td><td>-122.24</td><td>37.85</td><td>52</td><td>1467</td><td>190</td><td>496</td><td>177</td><td>7.2574</td><td>352100</td><td>NEAR BAY</td></tr><tr><td>3</td><td>-122.25</td><td>37.85</td><td>52</td><td>1274</td><td>235</td><td>558</td><td>219</td><td>5.6431</td><td>341300</td><td>NEAR BAY</td></tr><tr><td>4</td><td>-122.25</td><td>37.85</td><td>52</td><td>1627</td><td>280</td><td>565</td><td>259</td><td>3.8462</td><td>342200</td><td>NEAR BAY</td></tr><tr><td>5</td><td>-122.25</td><td>37.85</td><td>52</td><td>919</td><td>213</td><td>413</td><td>193</td><td>4.0368</td><td>269700</td><td>NEAR BAY</td></tr><tr><td>6</td><td>-122.25</td><td>37.84</td><td>52</td><td>2535</td><td>489</td><td>1094</td><td>514</td><td>3.6591</td><td>299200</td><td>NEAR BAY</td></tr><tr><td>7</td><td>-122.25</td><td>37.84</td><td>52</td><td>3104</td><td>687</td><td>1157</td><td>647</td><td>3.12</td><td>241400</td><td>NEAR BAY</td></tr><tr><td>8</td><td>-122.26</td><td>37.84</td><td>42</td><td>2555</td><td>665</td><td>1206</td><td>595</td><td>2.0804</td><td>226700</td><td>NEAR BAY</td></tr><tr><td>9</td><td>-122.25</td><td>37.84</td><td>52</td><td>3549</td><td>707</td><td>1551</td><td>714</td><td>3.6912</td><td>261100</td><td>NEAR BAY</td></tr><tr><td>10</td><td>-122.26</td><td>37.85</td><td>52</td><td>2202</td><td>434</td><td>910</td><td>402</td><td>3.2031</td><td>281500</td><td>NEAR BAY</td></tr><tr><td>11</td><td>-122.26</td><td>37.85</td><td>52</td><td>3503</td><td>752</td><td>1504</td><td>734</td><td>3.2705</td><td>241800</td><td>NEAR BAY</td></tr><tr><td>12</td><td>-122.26</td><td>37.85</td><td>52</td><td>2491</td><td>474</td><td>1098</td><td>468</td><td>3.075</td><td>213500</td><td>NEAR BAY</td></tr><tr><td>13</td><td>-122.26</td><td>37.84</td><td>52</td><td>696</td><td>191</td><td>345</td><td>174</td><td>2.6736</td><td>191300</td><td>NEAR BAY</td></tr><tr><td>14</td><td>-122.26</td><td>37.85</td><td>52</td><td>2643</td><td>626</td><td>1212</td><td>620</td><td>1.9167</td><td>159200</td><td>NEAR BAY</td></tr><tr><td>15</td><td>-122.26</td><td>37.85</td><td>50</td><td>1120</td><td>283</td><td>697</td><td>264</td><td>2.125</td><td>140000</td><td>NEAR BAY</td></tr><tr><td>16</td><td>-122.27</td><td>37.85</td><td>52</td><td>1966</td><td>347</td><td>793</td><td>331</td><td>2.775</td><td>152500</td><td>NEAR BAY</td></tr><tr><td>17</td><td>-122.27</td><td>37.85</td><td>52</td><td>1228</td><td>293</td><td>648</td><td>303</td><td>2.1202</td><td>155500</td><td>NEAR BAY</td></tr><tr><td>18</td><td>-122.26</td><td>37.84</td><td>50</td><td>2239</td><td>455</td><td>990</td><td>419</td><td>1.9911</td><td>158700</td><td>NEAR BAY</td></tr><tr><td>19</td><td>-122.27</td><td>37.84</td><td>52</td><td>1503</td><td>298</td><td>690</td><td>275</td><td>2.6033</td><td>162900</td><td>NEAR BAY</td></tr><tr><td>20</td><td>-122.27</td><td>37.85</td><td>40</td><td>751</td><td>184</td><td>409</td><td>166</td><td>1.3578</td><td>147500</td><td>NEAR BAY</td></tr></tbody></table>"
     },
     "output_type": "unknown"
    }
   ],
   "source": [
    "let housingData = DataFrame.LoadCsv(housingPath)\n",
    "housingData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "fsharp"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": "<table><thead><th>Index</th><th>Description</th><th>longitude</th><th>latitude</th><th>housing_median_age</th><th>total_rooms</th><th>total_bedrooms</th><th>population</th><th>households</th><th>median_income</th><th>median_house_value</th></thead><tbody><tr><td>0</td><td>Length (excluding null values)</td><td>20640</td><td>20640</td><td>20640</td><td>20640</td><td>20433</td><td>20640</td><td>20640</td><td>20640</td><td>20640</td></tr><tr><td>1</td><td>Max</td><td>-114.31</td><td>41.95</td><td>52</td><td>39320</td><td>6445</td><td>35682</td><td>6082</td><td>15.0001</td><td>500001</td></tr><tr><td>2</td><td>Min</td><td>-124.35</td><td>32.54</td><td>1</td><td>2</td><td>0</td><td>3</td><td>1</td><td>0.4999</td><td>14999</td></tr><tr><td>3</td><td>Mean</td><td>-119.569115</td><td>35.631866</td><td>28.639486</td><td>2635.7588</td><td>532.4762</td><td>1425.4779</td><td>499.53967</td><td>3.8706622</td><td>206854.97</td></tr></tbody></table>"
     },
     "output_type": "unknown"
    }
   ],
   "source": [
    "housingData.Description()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "fsharp"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": "<!DOCTYPE html>\r\n<div style=\"width: 900px; height: 500px;\" id=\"adb533de-9239-427e-8656-498dfe716a22\"></div><script type=\"text/javascript\">\r\n\nvar renderPlotly = function() {\n    var xplotRequire = require.config({context:'xplot-3.0.1',paths:{plotly:'https://cdn.plot.ly/plotly-1.49.2.min'}}) || require;\n    xplotRequire(['plotly'], function(Plotly) { \r\n\n            var data = 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          var layout = \"\";\n           Plotly.newPlot('adb533de-9239-427e-8656-498dfe716a22', data, layout);\n        \r\n});\n};\r\n// ensure `require` is available globally\r\nif ((typeof(require) !==  typeof(Function)) || (typeof(require.config) !== typeof(Function))) {\r\n    let require_script = document.createElement('script');\r\n    require_script.setAttribute('src', 'https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js');\r\n    require_script.setAttribute('type', 'text/javascript');\r\n    \r\n    \r\n    require_script.onload = function() {\r\n        renderPlotly();\r\n    };\r\n\r\n    document.getElementsByTagName('head')[0].appendChild(require_script);\r\n}\r\nelse {\r\n    renderPlotly();\r\n}\r\n\r\n</script>\r\n"
     },
     "output_type": "unknown"
    }
   ],
   "source": [
    "let graph =\n",
    "    Histogram(x = housingData.[\"median_house_value\"],\n",
    "              nbinsx = 20)\n",
    "graph |> Chart.Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "fsharp"
    }
   },
   "outputs": [
    {
     "data": {
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          var layout = \"\";\n           Plotly.newPlot('29472292-bb6b-4e2e-9460-fb4158d68506', data, layout);\n        \r\n});\n};\r\n// ensure `require` is available globally\r\nif ((typeof(require) !==  typeof(Function)) || (typeof(require.config) !== typeof(Function))) {\r\n    let require_script = document.createElement('script');\r\n    require_script.setAttribute('src', 'https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js');\r\n    require_script.setAttribute('type', 'text/javascript');\r\n    \r\n    \r\n    require_script.onload = function() {\r\n        renderPlotly();\r\n    };\r\n\r\n    document.getElementsByTagName('head')[0].appendChild(require_script);\r\n}\r\nelse {\r\n    renderPlotly();\r\n}\r\n\r\n</script>\r\n"
     },
     "output_type": "unknown"
    }
   ],
   "source": [
    "let graph =\r\n",
    "    Scattergl(\r\n",
    "        x = housingData.[\"longitude\"],\r\n",
    "        y = housingData.[\"latitude\"],\r\n",
    "        mode = \"markers\",\r\n",
    "        marker =\r\n",
    "            Marker(\r\n",
    "                color = housingData.[\"median_house_value\"],\r\n",
    "                colorscale = \"Jet\"))\r\n",
    "\r\n",
    "let plot = Chart.Plot(graph)\r\n",
    "plot.Width <- 600\r\n",
    "plot.Height <- 600\r\n",
    "display(plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare the training and validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "fsharp"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": "18576"
     },
     "output_type": "unknown"
    },
    {
     "data": {
      "text/html": "2064"
     },
     "output_type": "unknown"
    }
   ],
   "source": [
    "module Array = \n",
    "    let shuffle (arr: 'T[]) =\n",
    "        let rnd = Random()\n",
    "        let arr = Array.copy arr\n",
    "        for i in 0 .. arr.Length - 1 do\n",
    "            let r = i + rnd.Next(arr.Length - i)\n",
    "            let temp = arr.[r]\n",
    "            arr.[r] <- arr.[i]\n",
    "            arr.[i] <- temp\n",
    "        arr\n",
    "\n",
    "let randomIndices = [| 0 .. int housingData.Rows.Count - 1 |] |> Array.shuffle\n",
    "\n",
    "let testSize = int (float (housingData.Rows.Count) * 0.1)\n",
    "let trainRows = randomIndices.[testSize..]\n",
    "let testRows = randomIndices.[..testSize - 1]\n",
    "\n",
    "let housing_train = housingData.[trainRows]\n",
    "let housing_test = housingData.[testRows]\n",
    "\n",
    "display(housing_train.Rows.Count)\n",
    "display(housing_test.Rows.Count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the regression model and train it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "fsharp"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Wall time: 16434.0287ms"
     },
     "output_type": "unknown"
    }
   ],
   "source": [
    "#!time\n",
    "\n",
    "open Microsoft.ML\n",
    "open Microsoft.ML.Data\n",
    "open Microsoft.ML.AutoML\n",
    "\n",
    "let mlContext = MLContext()\n",
    "\n",
    "let experiment = mlContext.Auto().CreateRegressionExperiment(maxExperimentTimeInSeconds = 15u)\n",
    "let result = experiment.Execute(housing_train, labelColumnName = \"median_house_value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the training results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "fsharp"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": "<!DOCTYPE html>\r\n<div style=\"width: 900px; height: 500px;\" id=\"c1112a7c-44e3-4030-9487-9929d1be906a\"></div><script type=\"text/javascript\">\r\n\nvar renderPlotly = function() {\n    var xplotRequire = require.config({context:'xplot-3.0.1',paths:{plotly:'https://cdn.plot.ly/plotly-1.49.2.min'}}) || require;\n    xplotRequire(['plotly'], function(Plotly) { \r\n\n            var data = [{\"type\":\"scattergl\",\"x\":[1.356736],\"y\":[50576.32515535992],\"mode\":\"markers\",\"marker\":{\"size\":12},\"name\":\"SdcaRegression\"},{\"type\":\"scattergl\",\"x\":[0.716493,0.5385502,0.996715],\"y\":[30207.86109447825,32985.1041316028,40485.658928340235],\"mode\":\"markers\",\"marker\":{\"size\":12},\"name\":\"LightGbmRegression\"},{\"type\":\"scattergl\",\"x\":[1.1476915,0.6606083,1.8437742],\"y\":[30310.420452890343,107520.90191893773,30046.36799868915],\"mode\":\"markers\",\"marker\":{\"size\":12},\"name\":\"FastTreeRegression\"},{\"type\":\"scattergl\",\"x\":[1.0953389,2.2808337,3.6513284],\"y\":[31364.093976566546,29553.05263221776,31587.77091087843],\"mode\":\"markers\",\"marker\":{\"size\":12},\"name\":\"FastTreeTweedieRegression\"},{\"type\":\"scattergl\",\"x\":[0.9685265],\"y\":[49047.26656363283],\"mode\":\"markers\",\"marker\":{\"size\":12},\"name\":\"FastForestRegression\"},{\"type\":\"scattergl\",\"x\":[0.5395691],\"y\":[50266.94656266183],\"mode\":\"markers\",\"marker\":{\"size\":12},\"name\":\"LbfgsPoissonRegression\"},{\"type\":\"scattergl\",\"x\":[0.1526676],\"y\":[64462.23533790782],\"mode\":\"markers\",\"marker\":{\"size\":12},\"name\":\"OnlineGradientDescentRegression\"},{\"type\":\"scattergl\",\"x\":[0.2069284],\"y\":[49691.630890730194],\"mode\":\"markers\",\"marker\":{\"size\":12},\"name\":\"OlsRegression\"}];\n           var layout = {\"xaxis\":{\"title\":\"Training Time\",\"_isSubplotObj\":true},\"yaxis\":{\"title\":\"Error\",\"_isSubplotObj\":true}};\n           Plotly.newPlot('c1112a7c-44e3-4030-9487-9929d1be906a', data, layout);\n        \r\n});\n};\r\n// ensure `require` is available globally\r\nif ((typeof(require) !==  typeof(Function)) || (typeof(require.config) !== typeof(Function))) {\r\n    let require_script = document.createElement('script');\r\n    require_script.setAttribute('src', 'https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js');\r\n    require_script.setAttribute('type', 'text/javascript');\r\n    \r\n    \r\n    require_script.onload = function() {\r\n        renderPlotly();\r\n    };\r\n\r\n    document.getElementsByTagName('head')[0].appendChild(require_script);\r\n}\r\nelse {\r\n    renderPlotly();\r\n}\r\n\r\n</script>\r\n"
     },
     "output_type": "unknown"
    },
    {
     "data": {
      "text/plain": "Best Trainer:FastTreeRegression\r\n"
     },
     "output_type": "unknown"
    }
   ],
   "source": [
    "let scatters =\r\n",
    "    result.RunDetails\r\n",
    "     |> Seq.filter (fun d -> not (isNull d.ValidationMetrics))\r\n",
    "     |> Seq.groupBy (fun r -> r.TrainerName)\r\n",
    "     |> Seq.map (fun (name, details) ->\r\n",
    "                Scattergl(\r\n",
    "                    name = name,\r\n",
    "                    x = (details |> Seq.map (fun r -> r.RuntimeInSeconds)),\r\n",
    "                    y = (details |> Seq.map (fun r -> r.ValidationMetrics.MeanAbsoluteError)),\r\n",
    "                    mode = \"markers\",\r\n",
    "                    marker = Marker(size = 12)))\r\n",
    "\r\n",
    "let chart = Chart.Plot(scatters)\r\n",
    "chart.WithXTitle(\"Training Time\")\r\n",
    "chart.WithYTitle(\"Error\")\r\n",
    "display(chart)\r\n",
    "\r\n",
    "Console.WriteLine(\"Best Trainer:{0}\", result.BestRun.TrainerName);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validate and display the results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "dotnet_interactive": {
     "language": "fsharp"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": "<!DOCTYPE html>\r\n<div style=\"width: 900px; height: 500px;\" id=\"6c1acbd6-f22f-418e-a64e-75a0759de9af\"></div><script type=\"text/javascript\">\r\n\nvar renderPlotly = function() {\n    var xplotRequire = require.config({context:'xplot-3.0.1',paths:{plotly:'https://cdn.plot.ly/plotly-1.49.2.min'}}) || require;\n    xplotRequire(['plotly'], function(Plotly) { \r\n\n            var data = 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          var layout = {\"showlegend\":false,\"xaxis\":{\"title\":\"True Values\",\"_isSubplotObj\":true},\"yaxis\":{\"title\":\"Predicted Values\",\"_isSubplotObj\":true}};\n           Plotly.newPlot('6c1acbd6-f22f-418e-a64e-75a0759de9af', data, layout);\n        \r\n});\n};\r\n// ensure `require` is available globally\r\nif ((typeof(require) !==  typeof(Function)) || (typeof(require.config) !== typeof(Function))) {\r\n    let require_script = document.createElement('script');\r\n    require_script.setAttribute('src', 'https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.6/require.min.js');\r\n    require_script.setAttribute('type', 'text/javascript');\r\n    \r\n    \r\n    require_script.onload = function() {\r\n        renderPlotly();\r\n    };\r\n\r\n    document.getElementsByTagName('head')[0].appendChild(require_script);\r\n}\r\nelse {\r\n    renderPlotly();\r\n}\r\n\r\n</script>\r\n"
     },
     "output_type": "unknown"
    }
   ],
   "source": [
    "let testResults = result.BestRun.Model.Transform(housing_test)\r\n",
    "\r\n",
    "let trueValues = testResults.GetColumn<float32>(\"median_house_value\")\r\n",
    "let predictedValues = testResults.GetColumn<float32>(\"Score\")\r\n",
    "\r\n",
    "let predictedVsTrue =\r\n",
    "    Scattergl(\r\n",
    "        x = trueValues,\r\n",
    "        y = predictedValues,\r\n",
    "        mode = \"markers\")\r\n",
    "\r\n",
    "let maximumValue = Math.Max(Seq.max trueValues, Seq.max predictedValues)\r\n",
    "\r\n",
    "let perfectLine =\r\n",
    "    Scattergl(\r\n",
    "        x = [| 0.0f; maximumValue |],\r\n",
    "        y = [| 0.0f; maximumValue |],\r\n",
    "        mode = \"lines\")\r\n",
    "\r\n",
    "let chart = Chart.Plot([| predictedVsTrue; perfectLine |])\r\n",
    "chart.WithXTitle(\"True Values\")\r\n",
    "chart.WithYTitle(\"Predicted Values\")\r\n",
    "chart.WithLegend(false)\r\n",
    "chart.Width = 600\r\n",
    "chart.Height = 600\r\n",
    "display(chart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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     "language": "fsharp"
    }
   },
   "outputs": [],
   "source": []
  }
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